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Analysis of the interbeat interval increment to detect obstructive sleep apnoea/hypopnea
Frédéric Roche, MD,PhD; Sébastien Celle, MSc; Vincent Pichot, PhD; Jean-Claude Barthélémy, MD, PhD;
Emilia Sforza, MD, PhD;*
Service de Physiologie Clinique et de l�Exercice, Faculté de Médecine Jacques Lisfranc Université Jean Monnet, Saint-Etienne, France, * Sleep Laboratory, Department of Psychiatry, University Hospital, Geneva, Switzerland
Correspondance: Frédéric Roche, MD, PhD Laboratoire de Physiologie, EFCR CHU Nord - Niveau 6 F - 42055 Saint-Etienne Cedex 2 France
Phone: +33 4 77 82 83 00 Fax: +33 4 77 82 84 47 e-mail: [email protected]
Running title: Interbeat Interval Increment in OSAS diagnosis
. Published on February 14, 2007 as doi: 10.1183/09031936.00042606ERJ Express
Copyright 2007 by the European Respiratory Society.
2
Summary
The prevalence of sleep apnoea/hypopnoea syndrome (OSAS) is underestimated and
its diagnosis costly and restricted to specialised sleep laboratory. The frequency component of
interbeat interval increment (III) has been proposed as a simple and inexpensive diagnostic
tool in OSAS.
We analysed on a set of 150 patients with clinically suspected sleep-related breathing
disorder, the actual predictive accuracy of the power spectral density of the III of the very low
frequencies (%VLFI) by comparing with apnoea plus hypopnoea index (AHI) assessed by
synchronised polysomnography.
OSAS was defined in 100 patients according to an apnoea plus hypopnoea index >=
15/h. Receiver-operator characteristic curve built for %VLFI confirmed that this variable was
able to separate OSAS+ from OSAS- with statistical significance (W value: 0.70 ± 0.05).
Using an appropriate threshold, (>4%) %VLFI demonstrated a positive predictive value of
80%. Misclassification of false positive subjects occurred when the patient presented
significant sleep discontinuity and sleep fragmentation (sleep fragmentation index >=50/h)
related to insomnia or periodic limb movements.
A %VLFI>4 allows correct classification of OSAS when the clinical history suggested
sleep-related breathing disorders and when moderate-severe cases are considered. Higher
%VLFI may also indicate disrupted sleep in the absence of clear clinical symptoms of OSAS.
Key words: Sleep apnoea/hypopnoea syndrome, sleep fragmentation, ECG Holter,
autonomic nervous system activity, heart rate variability.
3
Introduction
Obstructive sleep apnoea syndrome (OSAS) is a common prevalent and probably
underestimated disorder (1) characterized by repetitive collapse of the upper airway during
sleep, with resulting hypoxemia, surges in sympathetic activity with arousals and cumulative
sleep fragmentation. OSAS has been associated with neuropsychological impairment,
increased risk of vehicular accidents (2) and cardiovascular consequences, such as
hypertension, myocardial infarction, stroke and increased mortality rates (3-4).
Since changes in autonomic system have been described during apnoea reflected by
increased heart rate variability and repetitive surges in sympathetic activity, efforts have been
made to develop non-invasive tools to diagnose OSAS using heart rate variability (HRV)
associated to sleep-related breathing disorders (5). In an era of continuously increasing
number of new methods for HRV determination and ongoing discussion about the relevance
of different HRV measures, the frequency component of interbeat interval increment (III) has
been introduced as a computationally simple and inexpensive tool in the screening of OSAS.
Applying this method, previous studies from our group demonstrated that a HRV variable
named �%VLFI� (6-7) is a useful criterion to establish a diagnosis of OSAS. Similar results
has recently been published in a chronic heart failure population (8) in that value of %VLFI
less than 2.4 excludes obstructive events. However, since a complex interaction of upper
airway obstruction, hypoxemia and sleep fragmentation occurs during obstructive apnoea,
modulation of disrupted sleep (9) and hypoxemia must also be considered in the interpretation
of III in the clinical setting.
The purpose of present study was, to determine the predictive accuracy of frequency-
domain parameters of the III, known to accurately extract the very low frequency
components, in a large group of patients referred for suspected sleep-related breathing
disorders. The accuracy of the method was evaluated regarding the most recent definition of
sleep-related breathing disorders considering the influence of co-morbidities, severity of sleep
fragmentation and associate vascular disease.
4
Methods
Study group: Patients undergoing polysomnography (PSG) for suspected OSAS were enrolled
in the study. Exclusion criteria were 1) presence of diseases not allowing analysis of HR
variability, i.e., permanent or paroxysmal atrial fibrillation, Shy-Drager syndrome,
polyneuropathy, permanent ventricular or atrial pacing (n=25); 2) current intake of
antiarrhythmic drugs or digitalis (n=10); and 3) previously treatment for OSAS by continuous
positive airway pressure therapy, surgery and/or oral device (n= 15). Of the initial sample,
150 patients (118 males, 32 females) with a mean (±SD) age of 51.3±9.7 yr., a mean body
mass index (BMI) of 30.4±5.3 kg/m2 fulfilled the inclusion criteria.
All patients underwent a detailed clinical interview with an experienced sleep specialist,
concerning the primary complaint motivating the consultation (i.e. snoring (42%), reported
apnoeas (70.5%) and sleepiness (11%)), present and past medical history, with special focus
on cardiac and cerebrovascular disease, hypertension, obstructive or restrictive lung disease,
metabolic disorders, psychiatric diseases and actual drug intake. Patients were informed that
some of the collected data would be used for research purposes and they gave written
informed consent.
Nocturnal sleep studies: Polysomnography included seven electroencephalograms, right and
left electrooculograms and one electromyogram of chin muscles for conventional sleep
staging. Respiratory airflow was monitored with a nasal cannula connected to a pressure
transducer (Protech2, Minneapolis, MN, USA), thoracic and abdominal respiratory
movements with piezoelectric strain gauges, and tracheal sound by microphone. Arterial
oxygen saturation (SaO2) was continuously measured with a finger oximeter. Sleep was
scored using the criteria of Rechtschaffen and Kales (10) for epochs of 20 s. by a scorer
experienced in the use of standard guidelines. As indices of sleep fragmentation, we defined
the number of awakenings, the number of sleep stage changes and the sleep fragmentation
index (11). The index of sleep fragmentation (SFI) was calculated as the total number of
sleep stage changes and awakenings lasting 20 sec divided by total sleep time per hour (11).
Respiratory events were scored using standard criteria (13). Hypopnoea were defined as a
50% or greater reduction in airflow from the baseline value lasting at least 10 s. and
associated with 4% desaturation or an arousal. Apnoeas were defined as the absence of
airflow on the nasal cannula lasting for >10 s. The apnoea + hypopnoea index (AHI) was
established as the ratio of the number of apnoeas and hypopnoea per hour of sleep. As indices
of nocturnal hypoxemia we considered the mean SaO2, the % of sleep time below 90% and
the minimal value recorded during sleep (Minimal SaO2). Patients were classified as having
OSAS if the AHI was >15 according to a previous report from our group (6-7).
5
Synchronized ECG Holter monitoring with interbeat interval increment analysis: Standard three-channels Holter tape recorders (Vista, Novacor, Rueil-Malmaison, France)
were used to acquire the data and were applied approximately 2 h before the beginning of the
polysomnographic recording and removed after the final awakening. The polygraphic data
were matched with the ECG Holter data using polygraphic clock time synchronized with
ECG Holter time before lights-out. Two independent scorers each one blinded to the results of
the other performed the heart rate variability analysis and the synchronized polysomnographic
scoring. The sampling rate of the ECG was 200 Hz. This ensured an accuracy of 5 ms for the
times of the identified R peaks. In order to obtain the RR interval we determined the time
when each QRS occurs by dedicated software (HolterSoft, Novacor, Rueil-MalMaison,
France). Ventricular and supraventricular ectopic beats, and artefacts defined by RR Interval
> 2000 ms, were automatically removed from analysis. The RR series were then built by
concatenation of the consecutive sections of normal RR sequences. Next, a cubic-spline
interpolated the irregularly spaced RR series onto a regularly spaced time base (sampling rate:
4 Hz). It should be noted that cubic spline interpolating smoothed the RR series and the first
derivative. For the whole night recording, the following cardiac autonomic activity measures
of HRV were performed according to published criteria (14) using frequency and time
domain analyses. Briefly, for frequency domain analysis we calculate the total power (Ptot),
the very low frequency (VLF: 0.00-0.04 Hz), the low frequency power (LF: 0.04-0.015 Hz)
and the high frequency power (HF: 0.15-0.40 Hz). LFnu and HFnu were calculated as LF and
HF divided by Ptot-VLF. The LF/HF ratio was also calculated. Overall the VLF represent
parasympathetic activity, LF and LFnu represent both sympathetic and parasympathetic
activity and HF and HFnu parasympathetic activity. Time domain methods allow us to assess
the standard deviation of the normal electrocardiographic R-R intervals (SDNN), the standard
deviation of the average normal electrocardiographic R-R intervals (SDANN), the square root
of the mean squared differences of successive R-R intervals (RMSSD), the number of interval
differences of successive R-R intervals (NN50) and the proportion derived by dividing NN50
by the total number of R-R intervals (pNN50). To identify the very-low frequency
oscillations, we performed a power spectral analysis of the Interbeat Interval Increment (III),
as a function of the inverse of the increment. The methodology of such analysis has been
extensively developed in previous studies (6-7). Briefly, III is defined by the backward
difference of RR values. So, III time series was obtained straightforwardly by taking the
difference between successive RR values sampling at 4 Hz. This last processing is similar to
estimating the first derivative of RR signal. To identify the very low frequency component of
III variations present in the recording we used Fourier transform. The III power spectral
6
density is estimated by dividing the III signal into successive blocks, and averaging squared-
magnitude Fourier transforms of the signal blocks. The block size was 4096 points i.e.
approximately 1000 sec. The present size ensures a Fourier frequency step largely smaller
than the VLFI frequency range. Thus, we calculated the %VLFI as the ratio of power spectral
in the very low frequency range i.e. from 0.01 to 0.05 beat-1 (VLFIpsd), over the total power
spectral density (0.01 to 0.5 beat-1, TIpsd). The %VLFI variable has been reported as the most
accurate variable predicting OSAS and was chosen as the only one we retained in the present
study in accordance with previous studies (6).
Statistical analysis: Data were analysed using StatView Software (SAS Institute Inc. Second
Edition, 1998) as well as SigmaPlot 10 Software (Systat Software, Inc. Point Richmond, CA,
USA) for ROC curves analysis. Differences in polygraphic and clinical findings between
patients without OSAS (OSAS-) and with OSAS (OSAS+) were analyzed using respectively
the Mann Whitney test and the chi-square test. To evaluate the ability of the %VLFI to
discriminate between diseased and non-diseased status according to several AHI thresholds
(>5, >10, >15, >30, >50), ROC curve analysis was used, with the areas under the curves
represented by the letter W and SE as well as the calculated CI. ROC curve analysis was also
used to evaluate the ability of the %VLFI to predict sleep fragmentation when a SFI> 50/h
was considered. The highest separation power was established to obtain a threshold value
with an optimised sensitivity or specificity for %VLFI according the highest W value
obtained in previous studies. The predictive accuracy of the %VLFI parameter was evaluated
using classical sensitivity, specificity and predictive values according to %VLFI threshold
values obtained in previous studies. Statistical significance was determined as p<0.05 after
Bonferroni correction. Values were expressed as mean and standard deviations (mean±SD).
7
Results
Table 1 shows the anthropometric and clinical characteristics of the patient groups. One
hundred patients were diagnosed as having OSAS (66.7%) using polysomnographic
recording, the remaining considered as not having the disease. OSAS patients were more
frequently men, were found to be older and they had higher BMI. Compared to OSAS-,
OSAS+ patients were referred more frequently for snoring and witnessed breathing pauses
during sleep, without differences for daytime sleepiness as a chief complaint. Detailed clinical
interview did not reveal significant differences between groups as to incidence of
cardiovascular, neurological, psychiatric and metabolic disease except hypertension affecting
more frequently patients with OSAS. Polysomnographic data of the two groups are reported
in Table 2. As expected the groups differed significantly in all parameters of sleep continuity
and sleep fragmentation, OSAS patients having lower amounts of slow wave sleep and REM
sleep and greater stage 1. Analysis of respiratory disturbances in the two groups revealed that
OSAS+ patients had higher AHI and more severe nocturnal hypoxemia. No differences in
subjective daytime sleepiness at the Epworth Sleepiness Scale were found between groups.
Table 3 summarises for each cardiac variable the average value during the sleep period in
OSAS- and OSAS+ patients. When the standard HRV measures were calculated within the
night in the two groups of patients, we observed no statistical significant difference. Only, the
frequency-domain III parameter (%VLFI) was found significantly higher in patients with
OSAS (mean±SD: 5.38±2.04 vs. 3.53±2.03%; p<0.0001). A weak but significant linear
correlation was found between AHI and %VLFI (R2=0.10; p<0.001). ROC curve (continuous
data) built for %VLFI confirmed that this variable was able to separate OSAS+ from OSAS-
status with statistical significance (W value= 0.70; p<0.0001) (Fig. 1) and to predict sleep
fragmentation (Fig.2) according to a SFI ≥50 (W value=0.68; p<0.0001). In order to see if
severity of the disease affect ability of %VLFI to discriminate AHI value ROC curve were
built according to AHI threshold. As summarised in Table 4 %VLFI appeared to be more
efficient to predict moderate to severe OSAS, W values higher when the AHI was >15 .Using
a %VLFI threshold of 2.4, sensitivity for OSAS prediction was 91%, specificity 34%, positive
predictive value 73.4% and negative predictive value reached 65.4% (True negatives: 17,
False negatives: 9, true positives: 91 and false positives: 33 patients). Using a %VLFI
threshold of 4, sensitivity for OSAS prediction was 64%, specificity 69.2%, positive
predictive value 80% and negative predictive value 48,6% (True negatives: 34, False
negatives: 36, true positives: 64, false positives: 16). Among the false positives group, an
enhanced sleep fragmentation index (mean±SD = 45.8±14.4/h) was founded. SFI excesses
8
50/h in 16 cases reporting insomnia and in 28 cases having periodic limb movements
independent of respiratory events (mean PLMS index: 28.4/±15). Using multiple logistic
regression analysis, after adjustment for age, gender and neck circumference, OSAS status
(AHI≥15/h) (OR: 2.6; 95%CI: 1.2-5.8; Chi2=6.65; p=0.009), sleep fragmentation as indicated
by a SFI ≥50/h (OR: 2.3; 95%CI: 1.3-5.2; Chi2=10.2, p=0.0014) appeared to be
independently and significantly associated with %VLFI>4.
9
Discussion
With both sleep quality and respiratory disorders taken into account, this study gives
new findings on previous observation on III pattern in patients with sleep-related breathing
disorders. First, the results presented here support the existence of differences in autonomic
control in patients with OSAS, VLF and %VLFI significantly higher in patients with
moderate to severe OSAS. Nevertheless, considering also hypoxemia and indices of sleep
fragmentation instead of analysing only AHI, our results suggest that differences in HRV are
not of the same magnitude within patients. Independently of frequency of sleep-disordered
breathing, a trend to increased sympathetic activity is observable when sleep is more
fragmented. Indeed, we suggest that determination of HRV may be affected by concomitant
sleep disorders inducing sleep fragmentation and sleep discontinuity. These results stress the
importance of considering the relationship between OSAS and sleep fragmentation when
HRV measurement is considered as a clinical tool in the diagnosis of sleep-related breathing
disorders.
The main goal of this study was to characterise the changes in HRV measurements
during sleep in patients with sleep-disordered breathing in order to establish if these measures
may indicate the occurrence of sleep apnoea syndrome. Here, we presented a novel method
for the detection of sleep-related breathing disorders, which overcomes the shortcoming of
spectral ECG analysis. The first important finding of this study was that while standard time-
and frequency- domain parameters were less sensitive to modification of autonomic control in
OSAS, %VLFI appears to be a more sensitive tool to detect not only the autonomic changes
associated with apnoea but also the severity of the disease. We confirm in a larger population
the high sensitivity and the negative predictive value of a low %VLFI (<2.4%) to exclude
OSAS and the high positive predictive value of %VLFI >4% to confirm diagnosis. Using a
same %VLFI threshold, similar results have been obtained in patients with central sleep apnea
(8) in which a %VLFI <2.4 ruled out association with OSAS with a specificity of 65% and a
sensitivity of 85%. For a %VLFI threshold of 2.4, sensitivity for OSAS prediction was 91%
but specificity 34%, indicating concomitant sleep alteration affecting heart rate variability. In
contrast, using a %VLFI threshold of 4, sensitivity for OSAS prediction was 64% and
specificity 69.2%. Thus, frequency-domain analysis of HRV could be considered as an easy
method of quantifying autonomic changes taking place during apnoeas.
Despite there is wide evidence that HRV reflects changes in autonomic system related to
apnoea, it is less clear whether the rise in sympathetic activity reflects only the apnoea effect
or it also affected by other factors, such as hypoxemia and sleep fragmentation, both
10
influencing autonomic system during sleep. The results of our study showed that the
magnitude of HRV changes were affected by sleep fragmentation, greater %VLFI found in
patients with higher sleep fragmentation index. Several studies have shown that analysis of
autonomic changes occurring during sleep could be applied to detect sleep fragmentation in
patients with sleep disorders, pulse transit time alterations (15-17), variation in tonometry and
blood pressure (18), all reflecting sleep discontinuity and sleep fragmentation.. The data of
our study, even though the SFI is a simple estimate of sleep fragmentation, indicate that
analysis of heart rate variability could be used as a surrogate of sleep quality, %VLFI greater
when sleep fragmentation and discontinuity were higher. Compared to other methods, the III
methodology represents a more simple and inexpensive tool to quantify the autonomic
nervous system activation when sleep is fragmented and disrupted (19), in relation to
spontaneous or evoked arousing stimuli (9,20-22) This result has important implications from
a clinical point of view in the diagnosis of sleep disorders. Although requiring replication
with a much larger and more carefully defined sleep fragmentation, such as microarousal
index, these data implicates that higher %VLFI may both indicate OSAS and severe sleep
fragmentation. Therefore, it is clear the relative lack of specificity of the present methodology
in the diagnosis of related sleep breathing disorders when concomitant sleep disorders, i.e.,
insomnia, restless legs syndrome, periodic leg movements, are present. This would also
suggest that III may be not a potential screening tool for sleep-related breathing disorders
when clinical history is uncertain and when association of other sleep disorders is postulated.
The current study presents some methodological limitations that need to be considered
in the discussion of our results. First, the present sample represents a fairly homogenous
apnoeic population who was in majority referred for snoring and apnoea. As a result, this
precludes generalization to other sleep disorders patients and the application of such described
algorithm of III in a general population could result in a significant alteration of the predictive
value of the %VLFI. Only a large study comparing %VLFI to conventional polysomnograghy
realized in a middle aged population with different sleep disorders could help to affirm the
role of III in the clinical screening of OSAS. Second, the threshold used to dichotomise OSAS
was more stringent that the usual AHI>5 criterion and limits the interpretation of %VLFI as a
screening tool for sleep-related breathing disorders, %VLFI being in our population an useful
marker to diagnose moderate to severe cases. It would be of interest in subsequent study to
evaluate whether III may diagnose the full spectrum of sleep-disordered breathing including
upper airway resistance syndrome and simple snoring. Finally some discrepancy in the cut-off
values ruling out OSAS where found in this population compared to previous studies (6), a
%VLFI<2.4 less specific to diagnose moderate-severe cases. Hourly %VLFI appeared to be
11
predictor of AHI and the prediction of an AHI>20 was more accurate for a %VLFI value of at
least 4%. Such threshold value could be appropriate since on previous data, positive
predictive accuracy could reach 80% in a high-risk population (7).
In conclusion, the described method of ECG variability provides a general framework
to detect and characterize ECG variability during obstructive apnoea and hypopnoeas.
Although this aim can be achieved by other methods such as pulse transit time (17) and
tonometry (18), the present method has the advantage that %VLFI obtained by simple non-
invasive ECG recording predicts the recognition of sleep apnea/hypopnea syndrome in two
third of our population. Combination of III criteria as a quantitative method of pulse rate rise
(23) after respiratory event and oximetry could represent a simple tool to screen OSAS (24).
Therefore, III seems to be a useful, simple and novel measure that could allow more clinicians
to identify patients with suspected sleep apnoea. However, since greater %VLFI may be
found when sleep is more fragmented, other sleep disorders could be suspected when %VLFI
is higher, stressing the need to propose in-laboratory studies when other sleep disorders are
hypothesized by clinical history. Future studies including larger sample of patients with
different sleep disorders are needed to identify pattern of heart rate variability in terms of
diagnosis, clinical features and degree of sleep alterations.
12
Acknowledgement: We are indebted to Odile Bigaignon, Norbert Grazian, Jean-René
Borderies, Gilles Ascher, Virginie Dauphinot for their assistance.
13
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16
Table 1. Anthropometric and clinical data of patients without (OSAS �) and with (OSAS +) obstructive sleep apnoea/hypopnoea syndrome (Mean±(SD))
OSAS-
(n=50)
OSAS +
(n=100)
P
Age yr. 48.5±8.5 52.8±10.0 0.03
Body Mass Index, kg/m2 29.5±6.0 30.9±4.9 0.04
Neck circumference, cm 39.9±3.6 42.1±3.4 0.02
Epworth Sleepiness Scale score 10.3±5.1 9.5±4.9 ns
Primary complaints
Snoring, % 68 40 <0.02
Apnoea, % 16 39 0.003
Sleepiness, % 8 12 ns
History of
Hypertension, % 24 40 0.04
Cardiovascular disease, % 4 6 ns
Diabetes, % 4 9 ns
Hypercholesterolemia, % 22 31 ns
ACE inhibitors, % 10 30 0.02
Beta-blockers, % 7 15 ns p : differences at the Chi-square and Mann-Whitney test between patient without and with OSAS
17
Table 2. Polysomnographic data of the population (Mean ±SD).
OSAS �
(n=50)
OSAS +
(n=100)
P
TST, min 435.9±49.5 417.8±78.6 ns
Stage 1, min 55.5±22.5 80.4±32.6 <0.0001
Stage 2, min 225.5±54.0 232.2±63.9 ns
Stage 3, min 59.6±31.7 39.8±30.8 0.001
Stage 4, min 15.4±19.7 7.5±13.8 0.006
Stage REM, min 80.1±28.8 58.0±31.5 <0.0001
WASO, min 69.5±43.2 106.6±64.3 0.001
SE, % 83.8±8.4 77.2±12.8 0.002
Sleep fragmentation index, n/h 42.5±14.3 75.1±44.1 <0.0001
PLMS index, n/h 9.5±19.1 12.0±16.4 ns
AHI, n/h 7.7±3.8 45.6±23.6 <0.0001
Min SaO2, % 83.9±4.4 77.4±8.9 <0.0001
Mean SaO2, % 93.3±1.6 92.0±3.0 0.01
Time SaO2 < 90, % 7.2±12.2 19.9±24.1 <0.0001
ODI, n/h 4.8±4.4 28.4±19.9 <0.0001
Snoring index, n/h 335.0±322.6 409.1±219.5 0.04 Legend: TST: total sleep time; WASO: wake after sleep onset; SE: sleep efficiency; AHI: apnea plus hypopnea index; PLMS index: periodic limb movements index; ODI: Oxygen desaturation index. p : differences at the Mann-Whitney test between patients without and with OSAS
18
Table3. Selected frequency domain measures of heart rate variability (HRV) during
sleep in patients without and with OSAS (Mean ± SD).
OSAS-
(n=50)
OSAS+
(n=100)
p
RR mean (ms) 878.5±113.9 919.4±117.7 ns
NN50 (n) 3977.7±4053.4 4073.5±3527.7 ns
PNN50 (%) 7.2±6.1 8.3±7.7 ns
SDANN (ms) 76.4±33.6 79.4±28.2 ns
SDNN (ms) 97.1±35.1 103.4±31.0 ns
SDNN Index (ms) 44.1±14.2 49.3±17.7 ns
RMSSD (ms) 34.3±23.1 33.7±14.8 ns
Ptot (ms2) 2248.3±1229.4 3043.2±2304.0 ns
VLF(ms2) 1328.2±5670.8 7157.8±5171.1 0.05
LF (ms2) 525.8±326.9 651.8±505.0 ns
HF (ms2) 261.3±241.2 288.3±231.8 ns
LF nu (n.u.) 79.8±1.7 65.4±11.6 ns
HF nu (n.u.) 42.8±783.4 34.6±11.6 ns
LF/HF ratio 3.40±2.2 3.45±2.1 ns
%VLFI 3.53±2.0 5.38±2.0 <0.0001 Legend: RR mean: Mean of the NN intervals; NN50 : numbers of pair of adjacent NN intervals differing by
more than 50 ms; pNN50: NN50 count divided by the total number of all NN intervals ; SDANN: standard
deviation of the averages of the NN intervals in all 5 min segeents of the entire period; SDNN: standard
deviation of all NN intervals; SDNN index: mean of the standard deviation of all NN intervals; RMSSD: the
square root of the mean of the squares of differences between adjacent NN intervals; Ptot: total power; VLF:
power in very low frequency; LF: power in low frequency; HF: power in high frequency; LFnu: LF power in
normalized units (its power spectrum over Ptot-VLFx100); HFnu: HF power in normalized units (its power
spectrum over Ptot-VLFx100); LF/HF ratio: ratio LF/HF; %VLFI: ratio of power spectral in the very low
frequency rangeover the total power spectral.
p : differences at the Mann-Whitney test between patients without and with OSAS
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Table 4. Evaluation of %VLFI to predict OSAS according to several AHI thresholds using ROC curve analysis. AHI threshold
W value SE 95% CI P value
5 0.65 0.05 0.55-0.75 0.007
10 0.65 0.05 0.57-0.74 0.001
15 0.70 0.04 0.61-0.79 <0.0001
30 0.69 0.05 0.59-0.79 0.0006
50 0.71 0.06 0.59-0.84 0.0005
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Figure 1. ROC curve for the prediction of OSAS (AHI of 15 or more) from %VLFI
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Figure 2. ROC Curve associating %VLFI and sleep fragmentation index (SFI>50)